Facial expression analysis based on machine learning requires large number of well-annotated data to reflect different changes in facial motion. Publicly available datasets truly help to accelerate research in this area by providing a benchmark resource, but all of these datasets, to the best of our knowledge, are limited to rough annotations for action units, including only their absence, presence, or a five-level intensity according to the Facial Action Coding System. To meet the need for videos labeled in great detail, we present a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D Facial Animation. One hundred and twenty-two participants, including children, young adults and elderly people, were recorded in real-world conditions. In addition, 99,356 frames were manually labeled using Expression Quantitative Tool developed by us to quantify 9 symmetrical FACS action units, 10 asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action descriptors and 2 asymmetrical FACS action descriptors, and each action unit or action descriptor is well-annotated with a floating point number between 0 and 1. To provide a baseline for use in future research, a benchmark for the regression of action unit values based on Convolutional Neural Networks are presented. We also demonstrate the potential of our FEAFA dataset for 3D facial animation. Almost all state-of-the-art algorithms for facial animation are achieved based on 3D face reconstruction. We hence propose a novel method that drives virtual characters only based on action unit value regression of the 2D video frames of source actors.
Background. Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death, and its biology remains poorly understood, especially in regards to the immunosuppression induced by immune checkpoints, such as Siglec-15. Most cancer treatments composed of immune checkpoint inhibitors and oncogene-targeted drugs display a better therapeutic effect in the clinic, including tumor progression inhibition and immunosuppression breaks. However, two or more drugs will result in a greater possibility of adverse effects. Thus, a double-function target is necessary for developing antitumor drugs, such as RNAi therapy. Methods. The expression of TUG1, Siglec-15, and miRNAs was evaluated by qPCR, and protein expression was analyzed by western blotting. The immune responses were evaluated by a Jurkat-reporter gene assay, a T cell-induced cytotoxicity assay, and IFN-γ/IL-2 release. The interactions among TUG1, Siglec-15, and miRNAs were verified by dual-luciferase reporter, RNA immunoprecipitation, and RNA pull-down assays. CCK-8 and Transwell assays were used to determine tumor cell proliferation, migration, and invasion. Results. In HCC patients and cells, increased TUG1 levels were observed, positively regulating Siglec-15 expression. TUG1-induced Siglec-15 upregulation resulted in the suppression of the immune response of HCC cells. hsa-miR-582-5p directly targeted TUG1 and Siglec-15 mRNA, and ihsa-miR-582-5p knockout prevented the regulation of Siglec-15 induced by THU1. Changes in hsa-miR-582-5p expression negatively regulated Siglec-15 levels and immunosuppression but had no influence on TUG1 levels. siRNA knockdown of TUG1 effectively led to tumor progression inhibition and immune response improvement in HCC cells both in vitro and in vivo. Conclusion. TUG1 increases the Siglec-15 level in HCC cells as a sponge to hsa-miR-582-5p, resulting in enhanced immunosuppression. TUG1 knockdown induced by siRNA not only reduces immunosuppression but also suppresses tumor progression both in vitro and in vivo. These novel findings may provide a potential and appropriate target for RNAi therapy to develop drugs with dual antitumor activity.
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